Microsoft is going bigger on its own silicon. CEO Satya Nadella announced what he called the company’s biggest bet yet on its own chips, including a new AI accelerator called Maia 300 and a plan to scale the Maia family to multi-gigawatt deployments starting next calendar year.
The announcement was made in a post on X on October 12, 2026. Here is what was said, what the numbers mean in plain terms, and which claims are still unverified. For more on the AI hardware race, see our Microsoft Maia 300 tag page.
Microsoft Maia 300 Chip: What Was Announced
| Item | Detail from Nadella’s announcement |
|---|---|
| New chip | Maia 300 |
| Efficiency claim (Maia 300) | 40% more tokens per watt than leading GPU-based systems |
| Maia 200 status | Already deploying across the US, Europe and Australia |
| Maia 200 claim | Up to 1.7x more tokens per watt across experiences in Teams and Copilot |
| Scale plan | Multi-gigawatt deployments of the Maia family starting next calendar year |
| Role | Primary inference system for Microsoft’s own products and services |
What Does Tokens Per Watt Mean?
AI models read and write in small units called tokens. Tokens per watt measures how much work a chip does for each unit of electricity. A higher number means cheaper and cooler AI, which matters because power and data-centre space are now among the biggest limits on AI growth. A 40% gain over leading GPU systems, if it holds in practice, would be a meaningful saving at Microsoft’s scale.
Maia 200 Is Already Running
Nadella said Maia 200 is already deploying across the United States, Europe and Australia, and that it delivers up to 1.7x more tokens per watt across experiences in Teams and Copilot. The words “up to” matter here, since the best case rarely matches the average workload.
Why This Move Matters
Cloud companies have relied heavily on GPUs from outside suppliers. By making Maia the primary inference system for its own products, Microsoft is signalling that it wants more control over cost and supply. Inference is the stage where a trained model answers questions, so it is where most daily AI usage happens. Running that on its own chips could lower costs for services like Copilot over time. Nadella also said Microsoft will keep bringing the latest innovations from across the ecosystem into its fleet, which suggests GPUs will not disappear from its data centres.
What Is Not Confirmed
The 40% and 1.7x figures are Microsoft’s own claims, shared in a social post without a published benchmark method, the models tested or the exact GPU systems used for comparison. Pricing, availability to outside customers and the precise chip specifications were not given. Total power and chip counts behind the multi-gigawatt plan were also not detailed. Wait for technical papers or independent tests before treating the numbers as settled. You can read more about the company on Microsoft’s website and Wikipedia.
Quick Answers
What is Maia 300?
Maia 300 is a new Microsoft AI accelerator announced by CEO Satya Nadella.
How many more tokens per watt does Maia 300 deliver?
Microsoft claims Maia 300 delivers 40% more tokens per watt than leading GPU-based systems. The text says the claim has not been verified by a published benchmark method or independent tests.
Is Microsoft Maia 200 already in use?
Nadella said Maia 200 is deploying across the United States, Europe and Australia. Microsoft claims it delivers up to 1.7 times more tokens per watt across experiences in Teams and Copilot.
When will Microsoft scale Maia deployments?
Microsoft plans to begin multi-gigawatt deployments of the Maia family next calendar year.
Details in early reports and social posts can change as companies publish official information, so please treat the figures above as reported at the time of writing. We will update this article when new or corrected details arrive, and we suggest checking the official pages linked here before you buy, invest or plan around anything mentioned in this story.
Source: Satya Nadella on X.
The bigger picture: Custom chips are becoming a core part of the AI race. Large cloud companies spend heavily on AI hardware, and even small efficiency gains can save a great deal of money and power when multiplied across many data centres. Microsoft’s message is that it wants to run most of its own inference on chips it designs, while still using outside hardware where it makes sense. Developers and businesses using Copilot or Azure should watch for any pricing or performance changes that follow, though none were announced in this post.
